Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

This work was aimed to explore the application of the L2-block-matching and 3-dimentional filtering (BM3D) (L2-BM3D) denoising algorithm in the treatment of lumbar degeneration with long- and short-segment fixation of posterior decompression. 120 patients with degenerative lumbar scoliosis were randomly divided into group A (MRI images were not processed), group B (MRI images were processed by the BM3D denoising algorithm), and group C (MRI images were processed by the BM3D denoising algorithm based on weighted norm L2). This denoising algorithm was comprehensively evaluated in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and running time. Besides, the results of surgeries based on different denoising methods were assessed through the surgical time, intraoperative blood loss, postoperative drainage, and postoperative follow-up. The results showed the following: (1) PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) of the L2-BM3D algorithm are better than those of the BM3D algorithm (31.21 dB versus 29.33 dB, 0.83 versus 0.72), while mean square error (MSE) was less than that of the BM3D algorithm ( P < 0.05 ). (2) The operation time, intraoperative bleeding, and postoperative drainage volume in group C were lower than those in group B and group A ( P < 0.05 ). The postoperative follow-up results showed that, in group C, the postoperative VAS (visual analysis scale) score (1.03 ± 0.29) and ODI (Oswestry disability index) (9.29 ± 0.32) were lower, indicating that the postoperative recovery effect of patients was better. Therefore, the patient’s postoperative recovery effect was better. In conclusion, the L2-BM3D algorithm had an ideal denoising effect on MRI images of lumbar degeneration and was worthy of clinical promotion.

Face occlusion removal for face recognition using the related face by structural similarity index measure and principal component analysis

Facial occlusions like sunglasses, masks, caps etc. have severe consequences when reconstructing the partially occluded regions of a facial picture. This paper proposes a novel hybrid machine learning approach for occlusion removal based on Structural Similarity Index Measure (SSIM) and Principal Component Analysis (PCA), called SSIM_PCA. The proposed system comprises two stages. In the first stage, a Face Similar Matrix (FSM) guided by the Structural Similarity Index Measure is generated to provide the necessary information to recover from the lost regions of the face image. The FSM generates Related Face (RF) images similar to the probe image. In the second stage, these RF images are considered as related information and used as input data to generate eigenspaces using PCA to reconstruct the occluded face region exploiting the relationship between the occluded region and related face images, which contain relevant data to recover from the occluded area. Experimental results with three standard datasets viz. Caspeal-R1, IMFDB, and FEI have proven that the proposed method works well under illumination changes and occlusion of facial images.

Computed Tomography Perfusion–Based Prediction of Core Infarct and Tissue at Risk: Can Artificial Intelligence Help Reduce Radiation Exposure?

Background and Purpose: We explored the feasibility of automated, arterial input function independent, vendor neutral prediction of core infarct, and penumbral tissue using complete and partial computed tomographic perfusion data sets through neural networks. Methods: Using retrospective computed tomographic perfusion data from 57 patients, split as training/validation (60%/40%), we developed and validated separate 2-dimensional U-net models for cerebral blood flow (CBF) and time to maximum (Tmax) maps calculation to predict core infarct and tissue at risk, respectively. Once trained, the full sets of 28 input images were sequentially reduced to equitemporal 14, 10, and 7 time points. The averaged structural similarity index measure between the model-derived images and ground truth perfusion maps was compared. Volumes for core infarct and Tmax were compared using the Pearson correlation coefficient. Results: Both CBF and Tmax maps derived using 28 and 14 time points had similar structural similarity index measure (0.80–0.81; P >0.05) when compared with ground truth images. The Pearson correlation for the CBF and Tmax volumes derived from the model using 28-tp with ground truth volumes derived from the RAPID software was 0.69 for CBF and 0.74 for Tmax. The predicted maps were fully concordant in terms of laterality to the commercial perfusion maps. The mean Dice scores were 0.54 for the core infarct and 0.63 for the hypoperfusion maps. Conclusion: Artificial intelligence model-derived volumes show good correlation with RAPID-derived volumes for CBF and Tmax. Within the constraints of a small sample size, the perfusion map quality is similar when using 14-tp instead of 28-tp. Our findings provide proof of concept that vendor neutral artificial intelligence models for computed tomographic perfusion processing using complete or partial image data sets appear feasible. The model accuracy could be further optimized using larger data sets.

No-Reference Digital Image Quality Assessment Based on Structure Similarity

Blind or non-referential image quality assessment (NR-IQA) indicates the problem of evaluating the visual quality of an image without any reference, Therefore, the need to develop a new measure that does not depend on the reference pristine image. This paper presents a NR-IQA method based on restoration scheme and a structural similarity index measure (SSIM). Specifically, we use blind restoration schemes for blurred images by reblurring the blurred image and then we use it as a reference image. Finally, we use the SSIM as a full reference metric. The experiments performed on standard test images as well as medical images. The results demonstrated that our results using a structural similarity index measure are better than other methods such as spectral kurtosis-based method.

SAR Image Change Detection via Multiple-Window Processing with Structural Similarity

In this paper, a synthetic aperture radar (SAR) change detection approach is proposed based on a structural similarity index measure (SSIM) and multiple-window processing (MWP). The proposed scheme is performed in two steps: (1) generation of a coherence image based on MWP associated with SSIM and (2) gamma correction (GC) filtering. The proposed method is capable of providing a high-quality coherence image because the MWP operation based on SSIM has high sensitivity to the similarity measure for intensity between two SAR images. By finding an optimum value of order of GC, the proposed method can considerably reduce the effect of speckle noise on the coherence image, while retaining nearly all the information related to changed region involved in the change detection map. Several experimental results are presented to demonstrate the effectiveness of the proposed scheme.

A Hybrid Digital Image Watermarking Scheme Incorporating DWT, DFT, DCT and SVD Transformations

Digital watermarking provides copyright protection and proof of ownership by inserting watermark metadata as owner’s identity in digital documents to prevent authenticity and copyright violations. The paper introduces a new hybrid image watermarking scheme by attaching multiple copies of watermarks in carrier image. The new scheme utilizes the advantages of DWT, DFT, DCT and SVD transformations to offer stable resistance in protecting watermark contents from various external attacks. The proposed scheme uses Haar wavelet, Fourier, Onion Peel Decomposition, DCT, zigzag ordering and SVD transforms to decompose the carrier image in to four levels to maintain imperceptibility in the watermarked images. The algorithm attaches replicas of watermark frequency blocks in all frequency components of host image to provide better robustness against external deprivations in watermarked images. The proposed algorithm also provides the increased probability of extracting at least one undamaged replica of watermark even when other frequencies are damaged by external attacks. The improved experimental results of the proposed scheme in terms of visual analysis and quantitative metrics on different images with different experimental set up demarcate that the proposed watermarking scheme provides stable performance in generating better watermarked images. It is experimentally found that the new scheme produces high quality watermarked images with an average of 7.62% lesser Mean Absolute Error (MAE) and increased Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index Measure (MSSIM) and Feature Similarity Index Measure (FSIM) of 5.02 %, 4.37 %, and 2.37 % respectively than the next best algorithms when simulated with 20 sets of watermark and cover images. The watermark images extracted by the proposed algorithm from extremely distorted watermarked images are with better visual and objective values than other methods used in the comparative study. Simulation analysis on 20 sets of watermark and cover images with 30 types of potential attacks reveals that the extracted watermark images through the proposed scheme produces an average of 5.62%, 6.37%, 5.75% improved Pearson Correlation Coefficients (PCC), Number of Changing Pixel Rate (NPCR) and the Unified Averaged Changed Intensity (UACI) values respectively than the next best algorithms used in the comparative study.

Bullying Victimization and Weapon Carrying: A Partial Test of General Strain Theory

Much of the existing research on adolescent firearm and weapon carrying lacks a theoretical framework. Relatedly, few studies have examined the relationship between weapon carrying and bullying victimization experienced at school, which has been established as a key strain in adolescence. The present study seeks to provide a partial test of general strain theory as a theoretical framework to explain adolescent weapon carrying. Using a large U.S. sample of 7th through 10th grade students ( n = 8,867), I find qualified support for general strain theory. While an index measure of bullying victimization was positively associated with weapon carrying as expected, two measures assessing specific forms of bullying victimization had nonlinear effects that are inconsistent with the theory. The proportion of one’s friends who carry weapons was consistently one of the strongest predictors of a respondent’s own weapon carrying. Implications and directions for future research are discussed.

Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.

Image Restoration Using Lucy Richardson Algorithm for Deblurring Images with Improved PSNR, SSIM, NC in Comparison with Wiener Filter

Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001).

Benchmark of the Trends of Spatial Inequality in World Megaregions

There have been growing concerns around the world over the rising spatial inequality (SI) amid fast and vast globalization. This paper presents an effort to benchmark the conditions and trends of spatial inequality in 37 megaregions in the United States, Europe, and China. Furthermore, the study selected three megaregion examples and analyzed the effect of developing high-speed rail (HSR) as an infrastructure investment strategy on reshaping the spatial pattern of job accessibility. The study measures spatial inequality with the Theil index of gross regional product and with the rank-size coefficient of polycentricity. Results show that spatial inequality exists and varies in magnitude within and between megaregions. On average, Chinese megaregions exhibited the level of spatial inequality about two times or more of those in the U.S. and European megaregions. The decade between 2006 and 2016 saw a decrease in the Theil index measure of megaregional inequality in China, but a slight increase in the United States and Europe. Fast growing megaregions exhibit high levels and rising trends of spatial inequality regardless of the country or continent setting. HSR helps improve mobility and accessibility; yet the extent to which HSR reduces spatial inequality is context dependent. This study presents a first attempt to assess and compare the spatial inequality conditions and trajectories in world megaregions aiming at promoting international learning.